4-D generative model for PET/MRI reconstruction

Stefano Pedemonte*, Alexandre Bousse, Brian F. Hutton, Simon Arridge, Sebastien Ourselin

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

    15 Citations (Scopus)


    We introduce a 4-dimensional joint generative probabilistic model for estimation of activity in a PET/MRI imaging system. The model is based on a mixture of Gaussians, relating time dependent activity and MRI image intensity to a hidden static variable, allowing one to estimate jointly activity, the parameters that capture the interdependence of the two images and motion parameters. An iterative algorithm for optimisation of the model is described. Noisy simulation data, modeling 3-D patient head movements, is obtained with realistic PET and MRI simulators and with a brain phantom from the BrainWeb database. Joint estimation of activity and motion parameters within the same framework allows us to use information from the MRI images to improve the activity estimate in terms of noise and recovery.

    Original languageEnglish
    Title of host publicationMedical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - 14th International Conference, Proceedings
    Number of pages8
    EditionPart 1
    Publication statusPublished - 2011
    Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
    Duration: 18 Sept 201122 Sept 2011

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print)03029743
    ISSN (Electronic)16113349


    Other14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011
    CityToronto, ON


    • Bayesian Networks
    • Emission Tomography
    • Molecular Imaging
    • Motion correction
    • Multi-modality


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